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hello_parallel_activity.py
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hello_parallel_activity.py
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import asyncio
from datetime import timedelta
from typing import List
from temporalio import activity, workflow
from temporalio.client import Client
from temporalio.worker import Worker
@activity.defn
async def say_hello_activity(name: str) -> str:
return f"Hello, {name}!"
@workflow.defn
class SayHelloWorkflow:
@workflow.run
async def run(self) -> List[str]:
# Run 5 activities at the same time
results = await asyncio.gather(
workflow.execute_activity(
say_hello_activity, "user1", start_to_close_timeout=timedelta(seconds=5)
),
workflow.execute_activity(
say_hello_activity, "user2", start_to_close_timeout=timedelta(seconds=5)
),
workflow.execute_activity(
say_hello_activity, "user3", start_to_close_timeout=timedelta(seconds=5)
),
workflow.execute_activity(
say_hello_activity, "user4", start_to_close_timeout=timedelta(seconds=5)
),
workflow.execute_activity(
say_hello_activity, "user5", start_to_close_timeout=timedelta(seconds=5)
),
)
# Sort the results because they can complete in any order
return list(sorted(results))
async def main():
# Start client
client = await Client.connect("localhost:7233")
# Run a worker for the workflow
async with Worker(
client,
task_queue="hello-parallel-activity-task-queue",
workflows=[SayHelloWorkflow],
activities=[say_hello_activity],
):
# While the worker is running, use the client to run the workflow and
# print out its result. Note, in many production setups, the client
# would be in a completely separate process from the worker.
result = await client.execute_workflow(
SayHelloWorkflow.run,
id="hello-parallel-activity-workflow-id",
task_queue="hello-parallel-activity-task-queue",
)
print(f"Result: {result}")
if __name__ == "__main__":
asyncio.run(main())